{"id":"W2132078810","doi":"10.1109/tcomm.2005.852845","title":"Analytical Modeling of Offset-Induced Priority in Multiclass OBS Networks","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Optical Network Technologies","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Offset (computer science); Robustness (evolution); Jitter; Scaling; Blocking (statistics); Computer science; Optical burst switching; Control theory (sociology); Mathematics; Wavelength; Telecommunications; Computer network; Physics; Wavelength-division multiplexing; Optical performance monitoring; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001148071,0.0001408853,0.0002239584,0.0001812532,0.00009548704,0.000009692321,0.0005588271,0.0001751804,0.00001143335],"category_scores_gemma":[0.00001442968,0.0001568324,0.00007589705,0.0005616954,0.0001097443,0.0001628299,0.000007049473,0.0007984685,0.00001312889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000163886,"about_ca_system_score_gemma":0.00001395686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001662205,"about_ca_topic_score_gemma":0.0005658767,"domain_scores_codex":[0.9990529,0.00003924335,0.0004199357,0.000135566,0.0001094038,0.0002429124],"domain_scores_gemma":[0.9983478,0.0003004407,0.00002548535,0.001222666,0.00005058578,0.00005298465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005688885,0.0001508741,0.00001966703,0.000005077969,0.00001984987,2.059509e-7,0.00004929763,0.9582787,0.0002537887,0.0008805944,0.000005185989,0.04033103],"study_design_scores_gemma":[0.0002568471,0.00001983585,0.00005849818,0.00004107674,0.00001943986,0.00000106761,0.00007832579,0.9983663,0.0006458176,0.0002329697,0.000141544,0.000138268],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07424332,0.0002446143,0.9230227,0.0005539698,0.00008462388,0.0002023648,0.000008136371,0.0004773066,0.001162978],"genre_scores_gemma":[0.9326068,0.0008630663,0.06641329,0.00001612572,0.00001213771,0.00005084164,0.000002707582,0.00002403514,0.00001100847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8583634,"threshold_uncertainty_score":0.639544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04112625151022743,"score_gpt":0.2853057520823505,"score_spread":0.2441795005721231,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}